Overview

Dataset statistics

Number of variables20
Number of observations5000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory781.4 KiB
Average record size in memory160.0 B

Variable types

NUM19
CAT1

Reproduction

Analysis started2020-08-25 02:03:08.745246
Analysis finished2020-08-25 02:04:00.549580
Duration51.8 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Variables

X00
Real number (ℝ)

Distinct count519
Unique (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.005144000000000001
Minimum-3.34
Maximum3.94
Zeros16
Zeros (%)0.3%
Memory size39.2 KiB
2020-08-25T02:04:00.595832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-3.34
5-th percentile-1.6505
Q1-0.68
median0.01
Q30.69
95-th percentile1.66
Maximum3.94
Range7.28
Interquartile range (IQR)1.37

Descriptive statistics

Standard deviation1.010130334
Coefficient of variation (CV)196.3705937
Kurtosis-0.1127278444
Mean0.005144
Median Absolute Deviation (MAD)0.685
Skewness-0.01952173776
Sum25.72
Variance1.020363292
2020-08-25T02:04:00.702618image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.39310.6%
 
-0.03290.6%
 
0.25270.5%
 
-0.33270.5%
 
0.57270.5%
 
0.34260.5%
 
-0.38260.5%
 
0.09260.5%
 
0.46260.5%
 
0.36260.5%
 
-0.52250.5%
 
-0.16250.5%
 
-0.17240.5%
 
-0.28240.5%
 
0.51240.5%
 
-0.71240.5%
 
-0.24240.5%
 
0.71240.5%
 
-0.27230.5%
 
-0.5230.5%
 
0.41230.5%
 
-0.08230.5%
 
0.72230.5%
 
-0.47220.4%
 
0.2220.4%
 
Other values (494)437687.5%
 
ValueCountFrequency (%) 
-3.341< 0.1%
 
-3.251< 0.1%
 
-31< 0.1%
 
-2.9540.1%
 
-2.931< 0.1%
 
-2.841< 0.1%
 
-2.821< 0.1%
 
-2.812< 0.1%
 
-2.81< 0.1%
 
-2.772< 0.1%
 
ValueCountFrequency (%) 
3.941< 0.1%
 
3.311< 0.1%
 
3.291< 0.1%
 
3.172< 0.1%
 
3.121< 0.1%
 
2.871< 0.1%
 
2.851< 0.1%
 
2.841< 0.1%
 
2.821< 0.1%
 
2.751< 0.1%
 

X17
Real number (ℝ)

Distinct count681
Unique (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0006220000000001
Minimum-4.08
Maximum6.2
Zeros10
Zeros (%)0.2%
Memory size39.2 KiB
2020-08-25T02:04:00.816982image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-4.08
5-th percentile-1.23
Q1-0.01
median0.94
Q31.96
95-th percentile3.4005
Maximum6.2
Range10.28
Interquartile range (IQR)1.97

Descriptive statistics

Standard deviation1.412815332
Coefficient of variation (CV)1.411937107
Kurtosis-0.2477703791
Mean1.000622
Median Absolute Deviation (MAD)0.99
Skewness0.1498366532
Sum5003.11
Variance1.996047163
2020-08-25T02:04:00.920570image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.25230.5%
 
0.43220.4%
 
-0.07210.4%
 
0.54210.4%
 
1.72200.4%
 
0.53190.4%
 
0.92190.4%
 
1.46190.4%
 
0.52190.4%
 
0.15190.4%
 
-0.16190.4%
 
1.12180.4%
 
1.55180.4%
 
0.86180.4%
 
1180.4%
 
0.99180.4%
 
0.44180.4%
 
0.51170.3%
 
1.45170.3%
 
0.6170.3%
 
-0.06170.3%
 
0.23170.3%
 
0.65170.3%
 
0.76170.3%
 
1.88170.3%
 
Other values (656)453590.7%
 
ValueCountFrequency (%) 
-4.081< 0.1%
 
-3.192< 0.1%
 
-3.151< 0.1%
 
-3.071< 0.1%
 
-3.031< 0.1%
 
-2.951< 0.1%
 
-2.912< 0.1%
 
-2.741< 0.1%
 
-2.71< 0.1%
 
-2.671< 0.1%
 
ValueCountFrequency (%) 
6.21< 0.1%
 
5.561< 0.1%
 
5.361< 0.1%
 
5.351< 0.1%
 
5.261< 0.1%
 
5.241< 0.1%
 
5.211< 0.1%
 
5.181< 0.1%
 
5.111< 0.1%
 
5.061< 0.1%
 

X15
Real number (ℝ)

Distinct count816
Unique (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0005040000000003
Minimum-2.99
Maximum7.86
Zeros5
Zeros (%)0.1%
Memory size39.2 KiB
2020-08-25T02:04:01.028103image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.99
5-th percentile-0.6905
Q10.64
median1.82
Q33.33
95-th percentile5.121
Maximum7.86
Range10.85
Interquartile range (IQR)2.69

Descriptive statistics

Standard deviation1.810684131
Coefficient of variation (CV)0.9051139767
Kurtosis-0.53636324
Mean2.000504
Median Absolute Deviation (MAD)1.315
Skewness0.2545971188
Sum10002.52
Variance3.278577021
2020-08-25T02:04:01.120385image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.18190.4%
 
0.91190.4%
 
0.81180.4%
 
1.6170.3%
 
1.73170.3%
 
0.85170.3%
 
1.23160.3%
 
1.46160.3%
 
0.77160.3%
 
0.12160.3%
 
1.99160.3%
 
0.92160.3%
 
1.74160.3%
 
2.33150.3%
 
0.7150.3%
 
2.78150.3%
 
3.45150.3%
 
1.91150.3%
 
2.39150.3%
 
0.68150.3%
 
1.45150.3%
 
2.05140.3%
 
0.58140.3%
 
0.73140.3%
 
2.92140.3%
 
Other values (791)460592.1%
 
ValueCountFrequency (%) 
-2.991< 0.1%
 
-2.71< 0.1%
 
-2.491< 0.1%
 
-2.481< 0.1%
 
-2.441< 0.1%
 
-2.411< 0.1%
 
-2.41< 0.1%
 
-2.361< 0.1%
 
-2.31< 0.1%
 
-2.221< 0.1%
 
ValueCountFrequency (%) 
7.861< 0.1%
 
7.551< 0.1%
 
7.391< 0.1%
 
7.151< 0.1%
 
7.111< 0.1%
 
7.11< 0.1%
 
7.091< 0.1%
 
7.081< 0.1%
 
6.891< 0.1%
 
6.861< 0.1%
 

X01
Real number (ℝ)

Distinct count546
Unique (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.338746
Minimum-3.25
Maximum3.88
Zeros17
Zeros (%)0.3%
Memory size39.2 KiB
2020-08-25T02:04:01.226080image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-3.25
5-th percentile-1.43
Q1-0.3725
median0.34
Q31.05
95-th percentile2.0905
Maximum3.88
Range7.13
Interquartile range (IQR)1.4225

Descriptive statistics

Standard deviation1.053656759
Coefficient of variation (CV)3.110462586
Kurtosis-0.03934868934
Mean0.338746
Median Absolute Deviation (MAD)0.71
Skewness0.003981559158
Sum1693.73
Variance1.110192566
2020-08-25T02:04:01.337750image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.62300.6%
 
0.14290.6%
 
-0.1280.6%
 
0.58270.5%
 
0.42270.5%
 
-0.17270.5%
 
0.48250.5%
 
0.59250.5%
 
0.88250.5%
 
0.33250.5%
 
0.63250.5%
 
0.68250.5%
 
0.2250.5%
 
0.51250.5%
 
1.25250.5%
 
0.07230.5%
 
0.41230.5%
 
0.61230.5%
 
-0.26230.5%
 
0.83220.4%
 
-0.02220.4%
 
0.45220.4%
 
0.26220.4%
 
1.1220.4%
 
0.86220.4%
 
Other values (521)438387.7%
 
ValueCountFrequency (%) 
-3.251< 0.1%
 
-3.121< 0.1%
 
-3.11< 0.1%
 
-2.961< 0.1%
 
-2.931< 0.1%
 
-2.921< 0.1%
 
-2.911< 0.1%
 
-2.861< 0.1%
 
-2.81< 0.1%
 
-2.781< 0.1%
 
ValueCountFrequency (%) 
3.881< 0.1%
 
3.831< 0.1%
 
3.611< 0.1%
 
3.51< 0.1%
 
3.461< 0.1%
 
3.41< 0.1%
 
3.382< 0.1%
 
3.361< 0.1%
 
3.341< 0.1%
 
3.331< 0.1%
 

X08
Real number (ℝ)

Distinct count762
Unique (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.672086
Minimum-3.38
Maximum7.9
Zeros3
Zeros (%)0.1%
Memory size39.2 KiB
2020-08-25T02:04:01.455337image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-3.38
5-th percentile-0.18
Q11.47
median2.81
Q33.94
95-th percentile5.19
Maximum7.9
Range11.28
Interquartile range (IQR)2.47

Descriptive statistics

Standard deviation1.663276708
Coefficient of variation (CV)0.6224637634
Kurtosis-0.4975666984
Mean2.672086
Median Absolute Deviation (MAD)1.23
Skewness-0.2313549011
Sum13360.43
Variance2.766489406
2020-08-25T02:04:01.551778image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4.22190.4%
 
4.02190.4%
 
3.43180.4%
 
2.39180.4%
 
3.26180.4%
 
3.19170.3%
 
3.15170.3%
 
2.36170.3%
 
3.01170.3%
 
3.55170.3%
 
3.61160.3%
 
2.09160.3%
 
3.87160.3%
 
2.57160.3%
 
1.07150.3%
 
4.29150.3%
 
3.86150.3%
 
2.93150.3%
 
2.84150.3%
 
3.6150.3%
 
2.61150.3%
 
3.39150.3%
 
3.59150.3%
 
2.91150.3%
 
3.5150.3%
 
Other values (737)459491.9%
 
ValueCountFrequency (%) 
-3.381< 0.1%
 
-2.61< 0.1%
 
-2.371< 0.1%
 
-2.171< 0.1%
 
-2.061< 0.1%
 
-2.052< 0.1%
 
-1.881< 0.1%
 
-1.872< 0.1%
 
-1.861< 0.1%
 
-1.831< 0.1%
 
ValueCountFrequency (%) 
7.91< 0.1%
 
7.771< 0.1%
 
6.881< 0.1%
 
6.721< 0.1%
 
6.552< 0.1%
 
6.541< 0.1%
 
6.492< 0.1%
 
6.472< 0.1%
 
6.461< 0.1%
 
6.442< 0.1%
 

X05
Real number (ℝ)

Distinct count808
Unique (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9973059999999998
Minimum-2.76
Maximum7.62
Zeros9
Zeros (%)0.2%
Memory size39.2 KiB
2020-08-25T02:04:01.657920image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.76
5-th percentile-0.69
Q10.59
median1.86
Q33.34
95-th percentile5.1605
Maximum7.62
Range10.38
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation1.814187371
Coefficient of variation (CV)0.9083171888
Kurtosis-0.602384373
Mean1.997306
Median Absolute Deviation (MAD)1.35
Skewness0.228318237
Sum9986.53
Variance3.291275818
2020-08-25T02:04:01.754698image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.24180.4%
 
3.4180.4%
 
0.8170.3%
 
1.65170.3%
 
0.86170.3%
 
0.35170.3%
 
1.11170.3%
 
1.99170.3%
 
1.41160.3%
 
0.97160.3%
 
2.08160.3%
 
1.35160.3%
 
2.15160.3%
 
2.1160.3%
 
-0.13160.3%
 
0.24150.3%
 
1.61150.3%
 
0.65150.3%
 
0.11150.3%
 
1.06150.3%
 
0.58150.3%
 
1.18140.3%
 
2.28140.3%
 
4.58140.3%
 
1.78140.3%
 
Other values (783)460492.1%
 
ValueCountFrequency (%) 
-2.761< 0.1%
 
-2.711< 0.1%
 
-2.652< 0.1%
 
-2.591< 0.1%
 
-2.551< 0.1%
 
-2.491< 0.1%
 
-2.481< 0.1%
 
-2.461< 0.1%
 
-2.412< 0.1%
 
-2.381< 0.1%
 
ValueCountFrequency (%) 
7.621< 0.1%
 
7.041< 0.1%
 
71< 0.1%
 
6.921< 0.1%
 
6.761< 0.1%
 
6.751< 0.1%
 
6.731< 0.1%
 
6.721< 0.1%
 
6.681< 0.1%
 
6.652< 0.1%
 

X11
Real number (ℝ)

Distinct count720
Unique (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.013614
Minimum-1.69
Maximum7.4
Zeros0
Zeros (%)0.0%
Memory size39.2 KiB
2020-08-25T02:04:01.866501image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.69
5-th percentile0.6
Q11.92
median3
Q34.0825
95-th percentile5.5505
Maximum7.4
Range9.09
Interquartile range (IQR)2.1625

Descriptive statistics

Standard deviation1.512447757
Coefficient of variation (CV)0.5018717584
Kurtosis-0.3695193242
Mean3.013614
Median Absolute Deviation (MAD)1.08
Skewness0.03352321264
Sum15068.07
Variance2.287498219
2020-08-25T02:04:01.976306image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3.2230.5%
 
2.33210.4%
 
3.95200.4%
 
2.55190.4%
 
2.71190.4%
 
3.11190.4%
 
2.68180.4%
 
3.5180.4%
 
3.77180.4%
 
3.29170.3%
 
3.02170.3%
 
3.7170.3%
 
2.35170.3%
 
2.48170.3%
 
2.12170.3%
 
3.82170.3%
 
3.57170.3%
 
3.41170.3%
 
3.62170.3%
 
1.81170.3%
 
2.52160.3%
 
3.84160.3%
 
3.68160.3%
 
2.76160.3%
 
3.49160.3%
 
Other values (695)455891.2%
 
ValueCountFrequency (%) 
-1.691< 0.1%
 
-1.611< 0.1%
 
-1.452< 0.1%
 
-1.431< 0.1%
 
-1.21< 0.1%
 
-1.191< 0.1%
 
-1.151< 0.1%
 
-1.051< 0.1%
 
-1.041< 0.1%
 
-12< 0.1%
 
ValueCountFrequency (%) 
7.41< 0.1%
 
7.321< 0.1%
 
7.31< 0.1%
 
7.291< 0.1%
 
7.241< 0.1%
 
7.231< 0.1%
 
7.181< 0.1%
 
6.971< 0.1%
 
6.951< 0.1%
 
6.941< 0.1%
 

X03
Real number (ℝ)

Distinct count684
Unique (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99161
Minimum-3.84
Maximum5.75
Zeros17
Zeros (%)0.3%
Memory size39.2 KiB
2020-08-25T02:04:02.078901image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-3.84
5-th percentile-1.24
Q1-0.02
median0.94
Q31.97
95-th percentile3.44
Maximum5.75
Range9.59
Interquartile range (IQR)1.99

Descriptive statistics

Standard deviation1.415238923
Coefficient of variation (CV)1.427213242
Kurtosis-0.3428401037
Mean0.99161
Median Absolute Deviation (MAD)0.99
Skewness0.1711365922
Sum4958.05
Variance2.002901208
2020-08-25T02:04:02.176823image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.2250.5%
 
0.48220.4%
 
1.63220.4%
 
1.48220.4%
 
0.83200.4%
 
0.18200.4%
 
-0.06200.4%
 
0.69200.4%
 
0.57200.4%
 
0.73200.4%
 
0.11200.4%
 
1.4190.4%
 
0.39190.4%
 
1.61190.4%
 
0.07190.4%
 
1.23190.4%
 
0.14190.4%
 
2.2180.4%
 
0.6180.4%
 
1.11180.4%
 
1.36180.4%
 
0.94180.4%
 
0.06180.4%
 
0.59180.4%
 
1.24180.4%
 
Other values (659)451190.2%
 
ValueCountFrequency (%) 
-3.841< 0.1%
 
-3.051< 0.1%
 
-2.791< 0.1%
 
-2.731< 0.1%
 
-2.641< 0.1%
 
-2.621< 0.1%
 
-2.611< 0.1%
 
-2.561< 0.1%
 
-2.541< 0.1%
 
-2.441< 0.1%
 
ValueCountFrequency (%) 
5.751< 0.1%
 
5.661< 0.1%
 
5.271< 0.1%
 
5.221< 0.1%
 
5.192< 0.1%
 
5.121< 0.1%
 
4.941< 0.1%
 
4.881< 0.1%
 
4.871< 0.1%
 
4.861< 0.1%
 

X18
Real number (ℝ)

Distinct count600
Unique (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.661482
Minimum-3.5
Maximum5.28
Zeros6
Zeros (%)0.1%
Memory size39.2 KiB
2020-08-25T02:04:02.283961image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-3.5
5-th percentile-1.24
Q1-0.18
median0.62
Q31.47
95-th percentile2.69
Maximum5.28
Range8.78
Interquartile range (IQR)1.65

Descriptive statistics

Standard deviation1.197325779
Coefficient of variation (CV)1.810065549
Kurtosis-0.1306869237
Mean0.661482
Median Absolute Deviation (MAD)0.83
Skewness0.1302695888
Sum3307.41
Variance1.433589021
2020-08-25T02:04:02.390078image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.35240.5%
 
1.11230.5%
 
0.32230.5%
 
0.35230.5%
 
1.03220.4%
 
1.49220.4%
 
0.53220.4%
 
-0.27220.4%
 
-0.1210.4%
 
0.42210.4%
 
0.33210.4%
 
0.3210.4%
 
0.64210.4%
 
1.1210.4%
 
0.6200.4%
 
-0.51200.4%
 
-0.09200.4%
 
0.5200.4%
 
1.06200.4%
 
0.38200.4%
 
0.62200.4%
 
-0.22200.4%
 
0.07200.4%
 
0.89200.4%
 
0.81200.4%
 
Other values (575)447389.5%
 
ValueCountFrequency (%) 
-3.51< 0.1%
 
-2.91< 0.1%
 
-2.851< 0.1%
 
-2.71< 0.1%
 
-2.681< 0.1%
 
-2.671< 0.1%
 
-2.631< 0.1%
 
-2.591< 0.1%
 
-2.571< 0.1%
 
-2.562< 0.1%
 
ValueCountFrequency (%) 
5.281< 0.1%
 
4.71< 0.1%
 
4.571< 0.1%
 
4.541< 0.1%
 
4.341< 0.1%
 
4.321< 0.1%
 
4.271< 0.1%
 
4.171< 0.1%
 
4.161< 0.1%
 
4.112< 0.1%
 

X16
Real number (ℝ)

Distinct count774
Unique (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.335032
Minimum-3.56
Maximum6.74
Zeros4
Zeros (%)0.1%
Memory size39.2 KiB
2020-08-25T02:04:02.493579image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-3.56
5-th percentile-1.21
Q10.07
median1.2
Q32.5325
95-th percentile4.21
Maximum6.74
Range10.3
Interquartile range (IQR)2.4625

Descriptive statistics

Standard deviation1.669948701
Coefficient of variation (CV)1.25086792
Kurtosis-0.5129952098
Mean1.335032
Median Absolute Deviation (MAD)1.22
Skewness0.2400949581
Sum6675.16
Variance2.788728665
2020-08-25T02:04:02.585896image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.62200.4%
 
-0.02190.4%
 
1.46180.4%
 
0.7180.4%
 
1.49180.4%
 
-0.18180.4%
 
1.17180.4%
 
0.48170.3%
 
-0.4170.3%
 
0.5170.3%
 
0.43170.3%
 
0.15170.3%
 
0.81160.3%
 
0.02160.3%
 
1.08160.3%
 
1.53160.3%
 
1.02160.3%
 
2.48160.3%
 
1.31160.3%
 
2.16150.3%
 
2.24150.3%
 
1.55150.3%
 
0.16150.3%
 
0.72150.3%
 
0.35150.3%
 
Other values (749)458491.7%
 
ValueCountFrequency (%) 
-3.561< 0.1%
 
-3.041< 0.1%
 
-2.881< 0.1%
 
-2.851< 0.1%
 
-2.821< 0.1%
 
-2.711< 0.1%
 
-2.71< 0.1%
 
-2.681< 0.1%
 
-2.651< 0.1%
 
-2.61< 0.1%
 
ValueCountFrequency (%) 
6.741< 0.1%
 
6.711< 0.1%
 
6.611< 0.1%
 
6.481< 0.1%
 
6.061< 0.1%
 
5.991< 0.1%
 
5.951< 0.1%
 
5.931< 0.1%
 
5.851< 0.1%
 
5.82< 0.1%
 

X13
Real number (ℝ)

Distinct count804
Unique (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.648632
Minimum-2.82
Maximum7.75
Zeros2
Zeros (%)< 0.1%
Memory size39.2 KiB
2020-08-25T02:04:02.869177image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.82
5-th percentile-0.27
Q11.36
median2.7
Q33.98
95-th percentile5.4105
Maximum7.75
Range10.57
Interquartile range (IQR)2.62

Descriptive statistics

Standard deviation1.760113078
Coefficient of variation (CV)0.6645366658
Kurtosis-0.5293333525
Mean2.648632
Median Absolute Deviation (MAD)1.305
Skewness-0.1075278957
Sum13243.16
Variance3.097998048
2020-08-25T02:04:02.965808image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2.57190.4%
 
2.12170.3%
 
3170.3%
 
3.14170.3%
 
4.22170.3%
 
3.91160.3%
 
2.94160.3%
 
1.74160.3%
 
3.04160.3%
 
3.02160.3%
 
2.55160.3%
 
3.19160.3%
 
2.31150.3%
 
3.98150.3%
 
3.28150.3%
 
1.97150.3%
 
2.61150.3%
 
2.39150.3%
 
3.16150.3%
 
3.82150.3%
 
4.55140.3%
 
1.62140.3%
 
4.38140.3%
 
1.69140.3%
 
1.67140.3%
 
Other values (779)461192.2%
 
ValueCountFrequency (%) 
-2.821< 0.1%
 
-2.381< 0.1%
 
-2.371< 0.1%
 
-2.311< 0.1%
 
-2.111< 0.1%
 
-2.051< 0.1%
 
-1.9730.1%
 
-1.921< 0.1%
 
-1.891< 0.1%
 
-1.881< 0.1%
 
ValueCountFrequency (%) 
7.751< 0.1%
 
7.381< 0.1%
 
71< 0.1%
 
6.981< 0.1%
 
6.951< 0.1%
 
6.931< 0.1%
 
6.922< 0.1%
 
6.861< 0.1%
 
6.851< 0.1%
 
6.821< 0.1%
 

X02
Real number (ℝ)

Distinct count608
Unique (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.672438
Minimum-4.2
Maximum4.72
Zeros14
Zeros (%)0.3%
Memory size39.2 KiB
2020-08-25T02:04:03.069508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-4.2
5-th percentile-1.21
Q1-0.15
median0.66
Q31.46
95-th percentile2.71
Maximum4.72
Range8.92
Interquartile range (IQR)1.61

Descriptive statistics

Standard deviation1.187969566
Coefficient of variation (CV)1.766660371
Kurtosis0.04031552954
Mean0.672438
Median Absolute Deviation (MAD)0.805
Skewness0.08413429481
Sum3362.19
Variance1.41127169
2020-08-25T02:04:03.166128image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.01250.5%
 
-0.01230.5%
 
1.03230.5%
 
0.68230.5%
 
0.37230.5%
 
0.06230.5%
 
0.75230.5%
 
0.38230.5%
 
1.36220.4%
 
0.55220.4%
 
0.26210.4%
 
0.33210.4%
 
0.72210.4%
 
0.83210.4%
 
0.58210.4%
 
0.84210.4%
 
0.85210.4%
 
0.1210.4%
 
0.01210.4%
 
1.42200.4%
 
0.86200.4%
 
1.02200.4%
 
0.39200.4%
 
0.73200.4%
 
1.1200.4%
 
Other values (583)446189.2%
 
ValueCountFrequency (%) 
-4.21< 0.1%
 
-4.051< 0.1%
 
-3.771< 0.1%
 
-3.561< 0.1%
 
-3.421< 0.1%
 
-3.371< 0.1%
 
-2.861< 0.1%
 
-2.831< 0.1%
 
-2.791< 0.1%
 
-2.741< 0.1%
 
ValueCountFrequency (%) 
4.721< 0.1%
 
4.651< 0.1%
 
4.581< 0.1%
 
4.541< 0.1%
 
4.52< 0.1%
 
4.391< 0.1%
 
4.352< 0.1%
 
4.31< 0.1%
 
4.141< 0.1%
 
4.091< 0.1%
 

X09
Real number (ℝ)

Distinct count725
Unique (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.988668
Minimum-1.79
Maximum7.63
Zeros4
Zeros (%)0.1%
Memory size39.2 KiB
2020-08-25T02:04:03.270832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.79
5-th percentile0.5295
Q11.88
median3
Q34.08
95-th percentile5.51
Maximum7.63
Range9.42
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.531505954
Coefficient of variation (CV)0.5124376325
Kurtosis-0.3822908178
Mean2.988668
Median Absolute Deviation (MAD)1.1
Skewness0.00278658638
Sum14943.34
Variance2.345510488
2020-08-25T02:04:03.364727image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2.46210.4%
 
3.01210.4%
 
3.13200.4%
 
4.19200.4%
 
2.82200.4%
 
3.9200.4%
 
3.33200.4%
 
2.58190.4%
 
4.47190.4%
 
3.4180.4%
 
2.19180.4%
 
3.41180.4%
 
3.71170.3%
 
3.42170.3%
 
2.98170.3%
 
3.09170.3%
 
2.48170.3%
 
3.93170.3%
 
3.2170.3%
 
3.96170.3%
 
1.79170.3%
 
2.91170.3%
 
2.01170.3%
 
2.03170.3%
 
2.37160.3%
 
Other values (700)454690.9%
 
ValueCountFrequency (%) 
-1.791< 0.1%
 
-1.771< 0.1%
 
-1.761< 0.1%
 
-1.651< 0.1%
 
-1.381< 0.1%
 
-1.241< 0.1%
 
-1.152< 0.1%
 
-1.141< 0.1%
 
-1.121< 0.1%
 
-1.11< 0.1%
 
ValueCountFrequency (%) 
7.631< 0.1%
 
7.531< 0.1%
 
7.491< 0.1%
 
7.361< 0.1%
 
7.251< 0.1%
 
7.211< 0.1%
 
7.171< 0.1%
 
7.161< 0.1%
 
7.081< 0.1%
 
7.061< 0.1%
 

X20
Real number (ℝ)

Distinct count525
Unique (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.021377999999999998
Minimum-3.88
Maximum4.01
Zeros15
Zeros (%)0.3%
Memory size39.2 KiB
2020-08-25T02:04:03.475209image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-3.88
5-th percentile-1.64
Q1-0.69
median-0.03
Q30.66
95-th percentile1.62
Maximum4.01
Range7.89
Interquartile range (IQR)1.35

Descriptive statistics

Standard deviation0.9970641246
Coefficient of variation (CV)-46.63972891
Kurtosis0.07450130276
Mean-0.021378
Median Absolute Deviation (MAD)0.67
Skewness0.0148283196
Sum-106.89
Variance0.9941368685
2020-08-25T02:04:03.585654image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.54300.6%
 
-0.57300.6%
 
0.27270.5%
 
-0.42260.5%
 
0.06260.5%
 
-0.4260.5%
 
-0.5260.5%
 
0.21260.5%
 
0.37260.5%
 
-0.25260.5%
 
-0.11250.5%
 
-0.1250.5%
 
0.24250.5%
 
0.32250.5%
 
-0.12240.5%
 
-0.18240.5%
 
-0.17240.5%
 
0.2240.5%
 
0.12240.5%
 
-0.06240.5%
 
0.44230.5%
 
-0.27230.5%
 
0.42230.5%
 
0.67230.5%
 
-0.02230.5%
 
Other values (500)437287.4%
 
ValueCountFrequency (%) 
-3.881< 0.1%
 
-3.821< 0.1%
 
-3.461< 0.1%
 
-3.452< 0.1%
 
-3.261< 0.1%
 
-3.221< 0.1%
 
-2.971< 0.1%
 
-2.961< 0.1%
 
-2.921< 0.1%
 
-2.831< 0.1%
 
ValueCountFrequency (%) 
4.011< 0.1%
 
3.671< 0.1%
 
3.321< 0.1%
 
3.11< 0.1%
 
3.031< 0.1%
 
2.991< 0.1%
 
2.981< 0.1%
 
2.952< 0.1%
 
2.941< 0.1%
 
2.931< 0.1%
 

X04
Real number (ℝ)

Distinct count766
Unique (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.310888
Minimum-3.48
Maximum6.5
Zeros12
Zeros (%)0.2%
Memory size39.2 KiB
2020-08-25T02:04:03.704913image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-3.48
5-th percentile-1.17
Q10.0375
median1.12
Q32.54
95-th percentile4.28
Maximum6.5
Range9.98
Interquartile range (IQR)2.5025

Descriptive statistics

Standard deviation1.67829146
Coefficient of variation (CV)1.280270671
Kurtosis-0.5154776857
Mean1.310888
Median Absolute Deviation (MAD)1.22
Skewness0.2998511551
Sum6554.44
Variance2.816662224
2020-08-25T02:04:03.797179image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2.2200.4%
 
0.61200.4%
 
0.92190.4%
 
0.06190.4%
 
-0.07190.4%
 
0.51180.4%
 
-0.09180.4%
 
0.46180.4%
 
0.12170.3%
 
0.4170.3%
 
0.15170.3%
 
0.94170.3%
 
0.36170.3%
 
1.12160.3%
 
0.14160.3%
 
0.26160.3%
 
0.84160.3%
 
1.95160.3%
 
-0.25160.3%
 
0.6160.3%
 
-0.46160.3%
 
1.65160.3%
 
1.09160.3%
 
-0.27150.3%
 
2.56150.3%
 
Other values (741)457491.5%
 
ValueCountFrequency (%) 
-3.481< 0.1%
 
-31< 0.1%
 
-2.941< 0.1%
 
-2.891< 0.1%
 
-2.861< 0.1%
 
-2.851< 0.1%
 
-2.751< 0.1%
 
-2.662< 0.1%
 
-2.621< 0.1%
 
-2.61< 0.1%
 
ValueCountFrequency (%) 
6.52< 0.1%
 
6.321< 0.1%
 
6.111< 0.1%
 
5.921< 0.1%
 
5.911< 0.1%
 
5.891< 0.1%
 
5.861< 0.1%
 
5.831< 0.1%
 
5.811< 0.1%
 
5.82< 0.1%
 

X12
Real number (ℝ)

Distinct count762
Unique (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6789080000000003
Minimum-2.61
Maximum7.5
Zeros6
Zeros (%)0.1%
Memory size39.2 KiB
2020-08-25T02:04:03.901023image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.61
5-th percentile-0.17
Q11.48
median2.83
Q33.9325
95-th percentile5.16
Maximum7.5
Range10.11
Interquartile range (IQR)2.4525

Descriptive statistics

Standard deviation1.651587829
Coefficient of variation (CV)0.6165153222
Kurtosis-0.5083572159
Mean2.678908
Median Absolute Deviation (MAD)1.2
Skewness-0.2436453753
Sum13394.54
Variance2.727742356
2020-08-25T02:04:04.004395image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3.93200.4%
 
3.13180.4%
 
3.61180.4%
 
3.14180.4%
 
4170.3%
 
2.12170.3%
 
1.83170.3%
 
3.22170.3%
 
3.69170.3%
 
2.92160.3%
 
3.28160.3%
 
3.34160.3%
 
3.42160.3%
 
3.78160.3%
 
1.56160.3%
 
2.94160.3%
 
3.89150.3%
 
2.34150.3%
 
3.39150.3%
 
1.81150.3%
 
2.66150.3%
 
3.55150.3%
 
4.34150.3%
 
2.78150.3%
 
3.56150.3%
 
Other values (737)459491.9%
 
ValueCountFrequency (%) 
-2.611< 0.1%
 
-2.21< 0.1%
 
-2.051< 0.1%
 
-1.971< 0.1%
 
-1.961< 0.1%
 
-1.951< 0.1%
 
-1.941< 0.1%
 
-1.891< 0.1%
 
-1.761< 0.1%
 
-1.721< 0.1%
 
ValueCountFrequency (%) 
7.51< 0.1%
 
7.041< 0.1%
 
6.981< 0.1%
 
6.921< 0.1%
 
6.851< 0.1%
 
6.81< 0.1%
 
6.751< 0.1%
 
6.671< 0.1%
 
6.621< 0.1%
 
6.572< 0.1%
 

X07
Real number (ℝ)

Distinct count791
Unique (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6592279999999997
Minimum-3.52
Maximum7.84
Zeros6
Zeros (%)0.1%
Memory size39.2 KiB
2020-08-25T02:04:04.110558image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-3.52
5-th percentile-0.2605
Q11.39
median2.72
Q33.94
95-th percentile5.42
Maximum7.84
Range11.36
Interquartile range (IQR)2.55

Descriptive statistics

Standard deviation1.74606687
Coefficient of variation (CV)0.6566066805
Kurtosis-0.501452967
Mean2.659228
Median Absolute Deviation (MAD)1.27
Skewness-0.09244733182
Sum13296.14
Variance3.048749514
2020-08-25T02:04:04.208572image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3.3180.4%
 
3.74180.4%
 
2.03180.4%
 
3.84180.4%
 
3.02180.4%
 
3.81170.3%
 
2.86170.3%
 
3.65170.3%
 
0.69170.3%
 
2.84170.3%
 
3.47170.3%
 
2.11160.3%
 
2.3160.3%
 
4.35160.3%
 
2.42160.3%
 
2.43160.3%
 
3.13160.3%
 
3.11160.3%
 
0.85160.3%
 
1.89160.3%
 
2.77150.3%
 
2.95150.3%
 
2.23150.3%
 
4.26150.3%
 
2.83150.3%
 
Other values (766)458991.8%
 
ValueCountFrequency (%) 
-3.521< 0.1%
 
-2.951< 0.1%
 
-2.261< 0.1%
 
-2.161< 0.1%
 
-2.151< 0.1%
 
-1.9130.1%
 
-1.940.1%
 
-1.881< 0.1%
 
-1.831< 0.1%
 
-1.741< 0.1%
 
ValueCountFrequency (%) 
7.841< 0.1%
 
7.671< 0.1%
 
7.571< 0.1%
 
7.321< 0.1%
 
7.311< 0.1%
 
7.251< 0.1%
 
7.151< 0.1%
 
7.11< 0.1%
 
7.091< 0.1%
 
7.031< 0.1%
 

X10
Real number (ℝ)

Distinct count770
Unique (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3366020000000005
Minimum-1.48
Maximum9.06
Zeros0
Zeros (%)0.0%
Memory size39.2 KiB
2020-08-25T02:04:04.317454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.48
5-th percentile0.82
Q12.04
median3.17
Q34.55
95-th percentile6.27
Maximum9.06
Range10.54
Interquartile range (IQR)2.51

Descriptive statistics

Standard deviation1.688632076
Coefficient of variation (CV)0.5060933478
Kurtosis-0.4510269844
Mean3.336602
Median Absolute Deviation (MAD)1.24
Skewness0.2830957034
Sum16683.01
Variance2.851478289
2020-08-25T02:04:04.411535image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.85200.4%
 
2.89190.4%
 
2.4180.4%
 
2.92180.4%
 
1.63180.4%
 
1.73180.4%
 
2.66170.3%
 
4.01170.3%
 
2.72170.3%
 
1.94160.3%
 
2.63160.3%
 
2.59160.3%
 
2.47160.3%
 
1.92160.3%
 
3.06160.3%
 
3.5150.3%
 
2.37150.3%
 
2.61150.3%
 
2.62150.3%
 
3.29150.3%
 
2.52150.3%
 
1.61150.3%
 
1.6150.3%
 
3.26150.3%
 
2.95150.3%
 
Other values (745)459291.8%
 
ValueCountFrequency (%) 
-1.481< 0.1%
 
-1.381< 0.1%
 
-1.031< 0.1%
 
-0.991< 0.1%
 
-0.861< 0.1%
 
-0.761< 0.1%
 
-0.661< 0.1%
 
-0.61< 0.1%
 
-0.591< 0.1%
 
-0.551< 0.1%
 
ValueCountFrequency (%) 
9.061< 0.1%
 
8.561< 0.1%
 
8.531< 0.1%
 
8.51< 0.1%
 
8.481< 0.1%
 
8.381< 0.1%
 
8.21< 0.1%
 
8.161< 0.1%
 
8.051< 0.1%
 
8.021< 0.1%
 

X14
Real number (ℝ)

Distinct count885
Unique (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.647668
Minimum-2.56
Maximum8.72
Zeros6
Zeros (%)0.1%
Memory size39.2 KiB
2020-08-25T02:04:04.515868image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.56
5-th percentile-0.42
Q11.12
median2.49
Q34.1825
95-th percentile6.0405
Maximum8.72
Range11.28
Interquartile range (IQR)3.0625

Descriptive statistics

Standard deviation2.018768339
Coefficient of variation (CV)0.7624703471
Kurtosis-0.6509845383
Mean2.647668
Median Absolute Deviation (MAD)1.53
Skewness0.1875629364
Sum13238.34
Variance4.075425607
2020-08-25T02:04:04.614049image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3.66180.4%
 
2.37170.3%
 
1.34170.3%
 
2.8170.3%
 
2.31160.3%
 
2.65160.3%
 
1.57160.3%
 
1.94160.3%
 
2.84160.3%
 
1.62160.3%
 
1.2150.3%
 
1.56150.3%
 
2.32150.3%
 
1.6150.3%
 
4.16150.3%
 
2.64150.3%
 
1.26150.3%
 
1.76140.3%
 
3.21140.3%
 
1.28140.3%
 
1.88140.3%
 
3.12140.3%
 
2.97130.3%
 
2.27130.3%
 
2.08130.3%
 
Other values (860)462192.4%
 
ValueCountFrequency (%) 
-2.561< 0.1%
 
-2.481< 0.1%
 
-2.291< 0.1%
 
-2.281< 0.1%
 
-2.191< 0.1%
 
-2.141< 0.1%
 
-2.121< 0.1%
 
-2.111< 0.1%
 
-2.11< 0.1%
 
-2.092< 0.1%
 
ValueCountFrequency (%) 
8.721< 0.1%
 
8.41< 0.1%
 
8.281< 0.1%
 
8.171< 0.1%
 
8.041< 0.1%
 
7.961< 0.1%
 
7.881< 0.1%
 
7.861< 0.1%
 
7.82< 0.1%
 
7.781< 0.1%
 

target
Categorical

Distinct count3
Unique (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
2
1696
0
1657
1
1647
ValueCountFrequency (%) 
2169633.9%
 
0165733.1%
 
1164732.9%
 
2020-08-25T02:04:04.761221image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
2169633.9%
 
0165733.1%
 
1164732.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number5000100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
2169633.9%
 
0165733.1%
 
1164732.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Common5000100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
2169633.9%
 
0165733.1%
 
1164732.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII5000100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
2169633.9%
 
0165733.1%
 
1164732.9%
 

Interactions

2020-08-25T02:03:10.263182image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:10.405299image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:10.555061image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:10.698869image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:10.842797image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:10.982982image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:11.124587image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:11.254929image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:11.391770image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:11.527091image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:11.665282image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:11.803004image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:11.939380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:12.081392image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:12.228447image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:12.361820image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:12.502952image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:12.644089image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:12.783213image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T02:03:13.066189image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:13.193783image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:13.323061image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:13.472442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:13.603240image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T02:03:13.854621image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:13.985480image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T02:03:14.948509image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T02:03:22.763828image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T02:03:23.016859image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:23.151946image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T02:03:23.737803image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T02:03:23.997677image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:24.143521image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T02:03:24.398589image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:24.522655image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:24.650566image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:24.775349image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:24.905403image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:25.032182image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:25.155155image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:25.284618image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:25.422103image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:25.548652image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:25.677182image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:25.812091image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:25.937317image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:26.062551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:26.192698image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:26.319093image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T02:03:35.012849image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:35.136218image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:35.263544image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:35.404728image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:35.529945image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:35.655239image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:35.800065image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:35.930990image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:36.067746image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:36.209035image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:36.335043image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:36.473673image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:36.804121image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:36.937944image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:37.067084image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:37.198096image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:37.328693image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:37.454109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:37.580091image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:37.710078image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:37.833775image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:37.967968image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:38.108637image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:38.235489image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:38.364702image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:38.498416image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:38.623853image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:38.750512image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:38.882624image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:39.011238image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:39.136797image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:39.267014image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:39.396135image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:39.523882image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:39.644055image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:39.765976image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:39.882873image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:40.014997image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:40.142630image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:40.262098image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:40.384803image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:40.521308image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:40.639846image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:40.764610image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:40.887781image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:41.202477image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:41.322243image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:41.453833image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:41.581382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:41.707447image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:41.839103image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:41.969619image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:42.105639image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:42.231422image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:42.359698image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:42.482025image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:42.615927image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:42.745426image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:42.869337image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:42.996837image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:43.138834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:43.263274image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:43.389813image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:43.520380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:43.653116image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:43.784760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:43.942315image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:44.087477image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:44.230043image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:44.378158image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:44.519312image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:44.664949image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:44.798194image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:44.938563image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:45.078377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:45.220201image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:45.361585image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:45.686718image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:45.829222image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:45.980719image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:46.122520image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:46.269056image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:46.415526image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:46.562146image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:46.712711image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:46.840446image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:46.961546image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:47.084077image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:47.214176image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:47.334926image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:47.459179image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:47.582679image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:47.708252image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:47.824497image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:47.951551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:48.083200image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:48.212651image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:48.337145image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:48.471574image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:48.592169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:48.721707image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:48.846625image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:48.969413image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:49.095389image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:49.234747image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:49.364861image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:49.498425image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:49.631705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:49.759794image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:49.886748image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:50.199481image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:50.327214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:50.450416image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:50.579880image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:50.710829image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:50.835729image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:50.962551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:51.100771image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:51.232889image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:51.358292image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:51.489968image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:51.614860image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:51.751378image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:51.893573image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:52.024748image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:52.159288image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:52.303229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:52.435513image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:52.565508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:52.693168image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:52.833720image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:52.959887image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:53.094753image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:53.227627image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:53.360735image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:53.492642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:53.634586image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:53.771601image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:53.920447image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:54.068071image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:54.204800image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:54.347387image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:54.671621image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:54.801589image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:54.929323image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:55.064182image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:55.191183image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:55.319430image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:55.444149image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:55.575096image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:55.698477image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:55.830972image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:55.957160image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:56.080090image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:56.208039image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:56.352071image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:56.478865image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:56.605844image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:56.738661image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:56.871495image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:56.996532image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:57.130681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:57.263137image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:57.391558image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:57.525761image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:57.698160image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:57.879581image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:58.004061image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:58.133436image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:58.255393image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:58.388644image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:58.518317image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:58.642705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:58.772553image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:58.918612image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:59.234319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:59.363878image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:59.499717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:03:59.624575image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T02:04:04.904614image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T02:04:05.207865image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T02:04:05.517135image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T02:04:05.809242image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T02:03:59.905117image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:04:00.371133image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

X00X17X15X01X08X05X11X03X18X16X13X02X09X20X04X12X07X10X14target
0-1.233.323.15-1.56-0.202.224.20-0.281.205.127.75-1.750.89-0.560.602.890.211.084.592
1-0.691.41-1.342.432.973.251.612.081.780.831.890.612.222.422.301.244.552.811.881
2-0.120.622.78-0.941.903.551.452.59-0.010.640.121.292.07-0.122.422.503.250.511.410
30.86-1.431.410.295.452.514.05-0.022.841.071.402.194.841.121.132.585.454.651.241
41.160.130.210.375.341.004.79-0.59-0.21-0.181.840.403.53-0.682.664.304.064.821.731
5-0.000.693.680.772.930.844.300.290.91-0.982.811.324.760.39-1.284.891.555.552.372
60.870.661.601.073.812.703.291.460.050.722.43-0.655.200.670.844.242.948.160.401
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8-1.111.514.56-1.141.640.445.680.002.903.184.24-0.891.75-0.120.533.392.153.923.812
9-0.75-0.41-0.251.102.620.406.941.43-1.500.733.23-1.904.501.470.470.753.516.831.082

Last rows

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49910.25-0.812.021.313.263.841.740.950.520.210.452.232.87-0.073.840.383.071.772.660
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4995-0.653.865.190.690.490.264.54-0.162.963.104.812.290.31-1.420.513.46-0.420.345.490
4996-0.02-0.56-1.560.674.924.771.503.180.02-1.182.522.343.39-0.182.161.534.542.561.141
49970.010.620.60-1.994.611.934.252.301.00-0.523.040.165.73-0.27-0.533.143.004.531.611
4998-0.401.722.630.412.20-0.664.851.042.083.833.62-0.480.591.370.793.640.520.855.970
49990.63-0.533.15-0.073.453.221.462.550.091.271.902.713.870.603.36-0.524.674.150.881